Entertainment

Toys

Ukulele chords classification 

Analysis of the vibrations produced by the instrument to detect the chord played.

Ukulele chords classification  Ukulele chords classification 
Ukulele chords classification  Ukulele chords classification 
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Entertainment

Toys

NanoEdge AI Studio

Human activity

Accelerometer

Read the tutorial
Systems interacts with their environment by emitting various signals. These signals are sources of relevant information reflecting equipment functioning. Being able to understand these signals allows significant optimization capabilities. Machine learning helps make the data generated by the system into meaningful data for Humans.

For example, here a ML library allows to classify vibration pattern to recognize music chords. This approach can easily be adapted to other application to be able to classify various events and then to make smarter solutions. 

Approach

We measured the vibration instead of the sound to reduce the impact of background noise 
After analysis, a frequency of 2000Hz permit to recognize a chord. We set the accelerometer to 3300Hz (minimum sensor frequency) 
We recorded examples of 20 different chords (100 signals per chords) 
We created an n-Class Classification model in NanoEdge AI Studio and tested it live on a NUCLEO-L432KC (and a STEVAL-MKI178V1 with LSM6DSL) 

Sensor

Accelerometer (3 axes): LSM6DSL

Data

20 classes of data 20 Ukulele chords
Signal length 3072 (1024* 3 axes)
Data rate 3300 Hz

Results

20 classes classification:
99.58% accuracy, 13.9 kB RAM, 82.9 kB Flash
Green points represent well classified signals. Red points represent misclassified signals. The classes are on the abscissa and the confidence of the prediction is on the ordinate 

Model created with

NanoEdge AI Studio

Model created with

Compatible with

STM32 series

Compatible with

Resources

Model created with NanoEdge AI Studio

A free AutoML software for adding AI to embedded projects, guiding users step by step to easily find the optimal AI model for their requirements.

Model created with NanoEdge AI Studio

Compatible with STM32 series

The STM32 family of 32-bit microcontrollers based on the Arm Cortex®-M processor is designed to offer new degrees of freedom to MCU users. It offers products combining very high performance, real-time capabilities, digital signal processing, low-power / low-voltage operation, and connectivity, while maintaining full integration and ease of development.

Compatible with STM32 series

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